Portable speckle imaging system and method for automated speckle activity map based dynamic speckle analysis

ABSTRACT

This disclosure relates to portable speckle imaging system and method for automated speckle activity map based dynamic speckle analysis. The embodiments of present disclosure herein address unresolved problem of capturing variations in speckle patterns where noise is completely removed and dependency on intensity of variations in speckle patterns is eliminated. The method of the present disclosure provides a correlation methodology for analyzing laser speckle images for applications such as seed viability, fungus detection, surface roughness analysis, and/or the like by capturing temporal variation from frame to frame and ignoring the intensity of speckle data after denoising, thereby providing an effective mechanism to study speckle time series data. The system and method of the present disclosure performs well in terms of time efficiency and visual cues and requires minimal human intervention.

PRIORITY CLAIM

This U.S. patent application claims priority under 35 U.S.C. § 119 to:India Application No. 202121047946, filed on Oct. 21, 2021. The entirecontents of the aforementioned application are incorporated herein byreference.

TECHNICAL FIELD

The disclosure herein generally relates to speckle analysis, and, moreparticularly, to portable speckle imaging system and method forautomated speckle activity map based dynamic speckle analysis.

BACKGROUND

Speckle imaging particularly laser speckle imaging is a well-establishedtechnique in multiple applications including surface roughness analysis,biological activity analysis such as fungus detection and seed viabilityanalysis in plants and blood flow analysis in humans. A speckle imagecould be obtained using a coherent light scattered from a target. If thetarget includes scatterers at a variety of different depths, thescattering causes originally coherent light to add constructively ordestructively depending on the various path lengths from the variousscatterers, resulting in an image with bright and dark spots which arereferred as speckles. Though, there exist methods for speckle imagingand analysis, some of them are highly dependent on intensity of specklepatterns and a few existing methods do not capture temporal variations.Thus, conventional systems and methods fail to perform well in varyingscenarios.

SUMMARY

Embodiments of the present disclosure present technological improvementsas solutions to one or more of the above-mentioned technical problemsrecognized by the inventors in conventional systems. For example, in oneembodiment, a portable laser speckle imaging system is provided. Thesystem comprising: a power source, a first light source with a holdersupport positioned to emit a beam towards a target object, an imagecapturing device positioned to receive illumination scattered from thetarget object, a second light source positioned in line with the imagecapturing device to enable white illumination on the target object, anattenuator to control intensity of the beam emitted by the first lightsource, a beam expander positioned between the first light source andthe attenuator to control size of the beam emitted by the first lightsource, a polarizer lens positioned between the attenuator and thetarget object to polarize the beam emitted by the first light source,and a controller unit operably connected to the first light source, theimage capturing device, and the second light source. In an embodiment,the controller unit comprises: one or more data storage devicesconfigured to store instructions; one or more communication interfaces;and one or more hardware processors operatively coupled to the one ormore data storage devices via the one or more communication interfaces,wherein the one or more hardware processors are configured by theinstructions to: acquire, a plurality of first type of images and asecond type of image of the target object from the image capturingdevice, wherein the plurality of first type of images are acquired whenthe target object is illuminated by the first light source and thesecond type of image is acquired when the target object is illuminatedby the second light source; perform, one or more masking operations onthe second type of image to obtain a masked image; obtain, by performingan automated speckle activity map based dynamic speckle analysistechnique on the plurality of first type of images, a third type ofimage, wherein obtaining the third type of image by performing theautomated speckle activity map based dynamic speckle analysis comprises:determining absolute difference between consecutive images in theplurality of first type of images to obtain a plurality of differenceimages; assigning, a value to each pixel of each of the plurality ofdifference images based on a comparison with a first threshold; addingthe plurality of difference images having a value assigned to each pixelto obtain a resultant image; and obtaining, based on a comparison ofeach pixel of the resultant image with a second threshold, the thirdtype of image; and determine a target image by multiplying the thirdtype of image with the masked image, wherein an area indicative of alevel of one or more activities occurring within the target object isdetected from the target image.

In another embodiment, a processor implemented method is provided. Themethod comprising acquiring, a plurality of first type of images and asecond type of image of the target object from the image capturingdevice, wherein the plurality of first type of images are acquired whenthe target object is illuminated by the first light source and thesecond type of image is acquired when the target object is illuminatedby the second light source; performing, one or more masking operationson the second type of image to obtain a masked image; obtaining, byperforming an automated speckle activity map based dynamic speckleanalysis technique on the plurality of first type of images, a thirdtype of image, wherein obtaining the third type of image by performingthe automated speckle activity map based dynamic speckle analysiscomprises: determining absolute difference between consecutive images inthe plurality of first type of images to obtain a plurality ofdifference images; assigning, a value to each pixel of each of theplurality of difference images based on a comparison with a firstthreshold; adding the plurality of difference images having a valueassigned to each pixel to obtain a resultant image; and obtaining, basedon a comparison of each pixel of the resultant image with a secondthreshold, the third type of image; and determining a target image bymultiplying the third type of image with the masked image, wherein anarea indicative of a level of one or more activities occurring withinthe target object is detected from the target image.

In yet another embodiment, a non-transitory computer readable medium isprovided. The non-transitory computer readable medium comprising:acquiring, a plurality of first type of images and a second type ofimage of the target object from the image capturing device, wherein theplurality of first type of images are acquired when the target object isilluminated by the first light source and the second type of image isacquired when the target object is illuminated by the second lightsource; performing, one or more masking operations on the second type ofimage to obtain a masked image; obtaining, by performing an automatedspeckle activity map based dynamic speckle analysis technique on theplurality of first type of images, a third type of image, whereinobtaining the third type of image by performing the automated speckleactivity map based dynamic speckle analysis comprises: determiningabsolute difference between consecutive images in the plurality of firsttype of images to obtain a plurality of difference images; assigning, avalue to each pixel of each of the plurality of difference images basedon a comparison with a first threshold; adding the plurality ofdifference images having a value assigned to each pixel to obtain aresultant image; and obtaining, based on a comparison of each pixel ofthe resultant image with a second threshold, the third type of image;and determining a target image by multiplying the third type of imagewith the masked image, wherein an area indicative of a level of one ormore activities occurring within the target object is detected from thetarget image.

In an embodiment, the first threshold is computed by: determining, in asimultaneous manner, a product of the plurality of first type of imageswith the masked image and an inverted masked image to obtain a pluralityof first type of masked images and a plurality of second type of maskedimages; determining absolute difference between consecutive images inthe plurality of first type of masked images and the second type ofmasked images to obtain a plurality of first type of resultant imagesand a plurality of second type of resultant images; obtaining a firsttype of array and a second type of array by performing a flatteningoperation on the plurality of first type of resultant images and theplurality of second type of resultant images; and determining a crossover point of histogram plots of the first type of array and the secondtype of array, wherein the cross over point is computed as the firstthreshold.

In an embodiment, the first light source is a coherent light source thatincludes a laser light emitter.

In an embodiment, the second light source includes at least one of (i) aring light that comprises an array of light emitting diodes arranged ina circular pattern, and (ii) a white light source with a fronttrasmittive diffuser.

In an embodiment, each of the plurality of first type of imagerepresents a laser speckle image and the second type of image representsa white light image.

In an embodiment, the one or more masking operations performed on thesecond type of image to obtain the masked image include backgroundsubtraction and thresholding.

In an embodiment, the inverted masked image is obtained by performing aforeground detection on the second type of image.

It is to be understood that both the foregoing general description andthe following detailed description are exemplary and explanatory onlyand are not restrictive of the invention, as claimed.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this disclosure, illustrate exemplary embodiments and, togetherwith the description, serve to explain the disclosed principles.

FIG. 1 illustrates an exemplary block diagram of a portable laserspeckle imaging system for automated speckle activity map based dynamicspeckle analysis according to some embodiments of the presentdisclosure.

FIGS. 2A and 2B illustrate an exemplary representation (not to scale) ofa left side view and a right side view of the portable laser speckleimaging system 100 for automated speckle activity map based dynamicspeckle analysis respectively according to some embodiments of thepresent disclosure.

FIG. 3 is an exemplary block diagram of a controller unit comprised inthe portable laser speckle imaging system for automated speckle activitymap based dynamic speckle analysis according to some embodiments of thepresent disclosure.

FIG. 4 is an exemplary block diagram illustrating functioning of thepower source and the controller unit comprised in the portable laserspeckle imaging system for automated speckle activity map based dynamicspeckle analysis according to some embodiments of the presentdisclosure.

FIG. 5 is an exemplary flow diagram illustrating a portable laserspeckle imaging method for automated speckle activity map based dynamicspeckle analysis according to some embodiments of the presentdisclosure.

FIG. 6 shows an example of a plurality of laser speckle images of atarget object according to some embodiments of the present disclosure.

FIG. 7 shows an example of a white light image of the target objectaccording to some embodiments of the present disclosure.

FIG. 8 shows an example of a masked image of the target object accordingto some embodiments of the present disclosure.

FIG. 9 shows an example of an inverted masked image of the target objectaccording to some embodiments of the present disclosure.

FIGS. 10A and 10B illustrate histogram plots of a maize seed and acoffee seed respectively to compute a first threshold value according tosome embodiments of the present disclosure.

FIG. 11 shows an example of an image obtained as an output of anautomated speckle activity map based bio dynamic speckle analysistechnique according to some embodiments of the present disclosure.

FIG. 12 shows an example of a target image to detect area indicative ofthe level of the one or more activities occurring within the targetobject according to some embodiments of the present disclosure.

FIGS. 13A through 13D provide a performance comparison of the method ofthe present disclosure with traditional methods for maize seed usingvisual maps according to some embodiments of the present disclosure.

FIGS. 14A through 14D provide a performance comparison of the method ofthe present disclosure with traditional methods for coffee seed usingvisual maps according to some embodiments of the present disclosure.

DETAILED DESCRIPTION

Exemplary embodiments are described with reference to the accompanyingdrawings. In the figures, the left-most digit(s) of a reference numberidentifies the figure in which the reference number first appears.Wherever convenient, the same reference numbers are used throughout thedrawings to refer to the same or like parts. While examples and featuresof disclosed principles are described herein, modifications,adaptations, and other implementations are possible without departingfrom the scope of the disclosed embodiments.

The present disclosure is directed to a portable speckle imaging systemand method for automated speckle activity map based dynamic speckleanalysis. The typical interpretation of results obtained fromconventional speckle analysis systems and methods has been modified tosolve a problem of capturing variations in speckle patterns where noiseis completely removed and dependency on intensity of variations inspeckle patterns is eliminated. Traditionally, speckle imaging systemsdetermine a correlation between adjacent frames. However, in a fewtraditional approaches, due to dependency on intensity, specklevariation at higher intensities get nullified. Further, there exists afew conventional approaches which try to capture overall temporalvariation ignoring frame to frame variations and few other approachesassume temporal correlation between each frame with every other framewithin a speckle sequence. This makes the existing methods to notperform well in varying scenarios. However, the method of the presentdisclosure captures the temporal variation from frame to frame byignoring the intensity of speckle data after denoising, thereby providesan effective mechanism to study speckle time series data.

In the context of the present disclosure, the expressions ‘image’, and‘frame’ may be used interchangeably. Although further description of thepresent disclosure is directed to speckle imaging technique andspecifically laser speckle imaging technique, it may be noted that thedescribed application is non-limiting and systems and methods of thepresent disclosure may be applied in any domain such as plant healthanalysis, biomedical imaging, paint analysis using surface roughnessanalysis.

Referring now to the drawings, and more particularly to FIGS. 1 through14D, where similar reference characters denote corresponding featuresconsistently throughout the figures, there are shown preferredembodiments and these embodiments are described in the context of thefollowing exemplary system and/or method.

Reference numerals of one or more components of the portable laserspeckle imaging system as depicted in the FIG. 1 are provided in Table 1below for ease of description:

TABLE 1 Sr. No. Component Reference numeral 1 Power source 102 2 Firstlight source 104 3 Target object 106 4 Image capturing device 108 5Second light source 110 6 Attenuator 112 7 Beam expander 114 8 Polarizerlens 116 9 Controller unit 118 10 Data storage device/Memory  118A 11Communication interface  118B 12 Hardware processor  118C

FIG. 1 illustrates an exemplary block diagram of a portable laserspeckle imaging system for automated speckle activity map based dynamicspeckle analysis according to some embodiments of the presentdisclosure. In an embodiment, the portable laser speckle imaging system100 includes a power source 102 which may include but not limited to abattery, a first light source 104 with a holder support positioned toemit a beam towards a target object 106, an image capturing device 108positioned to receive illumination scattered from the target object 106,an attenuator 112 to control intensity of the beam emitted by the firstlight source 104, a beam expander 114 positioned between the first lightsource 104 and the attenuator 112 to control size of the beam emitted bythe first light source 104, a polarizer lens 116 positioned between theattenuator 112 and the target object 106 to polarize the beam emitted bythe first light source 104, and a controller unit 118 operably connectedto the first light source 104, the image capturing device 108 and thesecond light source 110. FIGS. 2A and 2B illustrate an exemplaryrepresentation (not to scale) of a left side view and a right side viewof the portable laser speckle imaging system 100 for automated speckleactivity map based dynamic speckle analysis respectively according tosome embodiments of the present disclosure.

In an embodiment, the first light source is a coherent light source thatincludes a laser light emitter. Here, the laser light emitter(alternatively referred as laser diode) emits the beam within afrequency range of 400 nm to 700 nm. Further, the holder supports arerequired to hold the components of the portable laser speckle imagingsystem in a stable manner for easy imaging. In an embodiment, the imagecapturing device 108 may include but not limited to a camera such as adslr camera with good wide angle or macro lens. In an embodiment, thesecond light source 110 includes at least one of (i) a ring light thatcomprises an array of light emitting diodes arranged in a circularpattern and (ii) a white light source with a front trasmittive diffuser.The ring light is used to avoid the shadows in the target object beingimaged and can be powered by 5 v 500 mA usb output. In context of thepresent disclosure, the target object is a seed specimen that needs tobe analyzed and is kept on a colored paper (say green paper) placed on awooden platform. Here the green paper acts as backdrop for separatingthe seed specimen from background, thereby providing better backgroundsubtraction. In an embodiment, the beam expander is similar to amicroscopic device with 40× magnification and the polarizer lens couldbe an infrared (IR) filter. In an embodiment, the laser diode, the beamexpander, the attenuator and the polarizer lens are translatable androtatable along the holder and each of them are removable andreplaceable keeping other components intact.

In an embodiment, the controller unit may include but not limited to awired controller such as Arduino® board, a Bluetooth® controller, a WiFicontroller and/or a combination thereof. FIG. 3 is an exemplary blockdiagram illustrating functioning of the power source and the controllerunit comprised in the portable laser speckle imaging system forautomated speckle activity map based dynamic speckle analysis accordingto some embodiments of the present disclosure. In an embodiment, thecamera is directly connected to the battery and can be controlled byBluetooth® or wired i2c communication from the Arduino® board. Thecontroller unit is powered by 5 v 5000 mah battery. The Bluetooth®controller such as HC05 and/or the like is interfaced to the Arduino®controller as shown in FIG. 3 . Further, power to the first light sourceand the second light source is supplied through power out gpio pins ofthe Arduino® controller. The Arduino® controller is connected to thecamera via Bluetooth® and constantly communicates. In an embodiment, thecontroller unit 118 controls turning on and off of the image capturingdevice, the first light source and the second light source.

FIG. 4 illustrates an exemplary block diagram of the controller unit 118comprised in the portable laser speckle imaging system 100 for automatedspeckle activity map based dynamic speckle analysis according to someembodiments of the present disclosure. As shown in FIG. 3 , thecontroller unit 118 further comprises one or more data storage devicesor memory 118A configured to store instructions, one or morecommunication interfaces 118B, and one or more hardware processors 118Coperatively coupled to the one or more data storage devices 118A via theone or more communication interfaces 116B.

The one or more hardware processors 118C can be implemented as one ormore microprocessors, microcomputers, microcontrollers, digital signalprocessors, central processing units, state machines, graphicscontrollers, logic circuitries, and/or any devices that manipulatesignals based on operational instructions. Among other capabilities, theprocessor(s) are configured to fetch and execute computer-readableinstructions stored in the memory. In the context of the presentdisclosure, the expressions ‘processors’ and ‘hardware processors’ maybe used interchangeably. In an embodiment, the one or more hardwareprocessors 118C can be implemented in a variety of computing systems,such as laptop computers, notebooks, hand-held devices, workstations,mainframe computers, servers, a network cloud and the like.

In an embodiment, the communication interface(s) or input/output (I/O)interface(s) 118B may include a variety of software and hardwareinterfaces, for example, a web interface, a graphical user interface,and the like and can facilitate multiple communications within a widevariety of networks N/W and protocol types, including wired networks,for example, LAN, cable, etc., and wireless networks, such as WLAN,cellular, or satellite. In an embodiment, the I/O interface(s) caninclude one or more ports for connecting a number of devices to oneanother or to another server.

The one or more data storage devices or memory 118A may include anycomputer-readable medium known in the art including, for example,volatile memory, such as static random access memory (SRAM) and dynamicrandom access memory (DRAM), and/or non-volatile memory, such as readonly memory (ROM), erasable programmable ROM, flash memories, harddisks, optical disks, and magnetic tapes.

In an embodiment, the one or more hardware processors 118C can beconfigured to perform a portable laser speckle imaging method forautomated speckle activity map based dynamic speckle analysis, which canbe carried out by using methodology, described in conjunction with FIG.5 , and use case examples.

FIG. 5 is an exemplary flow diagram illustrating a portable laserspeckle imaging method for automated speckle activity map based dynamicspeckle analysis using the controller unit 118 of FIG. 3 comprised inthe system 100 of FIG. 1 , according to some embodiments of the presentdisclosure. In an embodiment, the controller unit 118 comprises one ormore data storage devices or the memory 118A operatively coupled to theone or more processors 118C and is configured to store instructions forexecution of steps of the method 500 by the one or more processors 118C.The steps of the method 500 of the present disclosure will now beexplained with reference to the components or blocks of the system 100as depicted in FIG. 1 , and the steps of flow diagram as depicted inFIG. 3 . Although process steps, method steps, techniques or the likemay be described in a sequential order, such processes, methods andtechniques may be configured to work in alternate orders. In otherwords, any sequence or order of steps that may be described does notnecessarily indicate a requirement that the steps to be performed inthat order. The steps of processes described herein may be performed inany order practical. Further, some steps may be performedsimultaneously.

Referring to the steps of the method 500 depicted in FIG. 5 , in anembodiment of the present disclosure, at step 502, the one or morehardware processors 118C are configured to acquire a plurality of firsttype of images and a second type of image of the target object from theimage capturing device. In an embodiment, the plurality of first type ofimages are acquired when the target object is illuminated by the firstlight source and the second type of image is acquired when the targetobject is illuminated by the second light source. In an embodiment, thefirst type of image represents a laser speckle image and the second typeof image represents a white light image. Here, the white light imagerepresents a standard RGB image acquired by the camera undernatural/white light. FIG. 6 shows an example of a plurality of laserspeckle images of a target object according to some embodiments of thepresent disclosure. FIG. 7 shows an example of a white light image ofthe target object according to some embodiments of the presentdisclosure. In an embodiment, when the battery is turned on, Bluetoothcommunication between the Arduino® controller and the camera isestablished. Further, inputs to the first light source and the secondlight source are connected to the battery through the Arduino®controller, all components comprised in the portable laser speckleimaging system 100 are aligned and settings of the camera such asshutter speed, International Organization of Standardization (ISO), andaperture are fixed. Initially, both the first light source and thesecond light source are off and a record command in the camera isinitiated by the Arduino® uno controller. Then same controller turns ONthe first light source and the camera captures a series of laser speckleimages (say 150-200 images), which are referred to as first type ofimages. Further, the Arduino® controller turns OFF the first lightsource and turns ON the second light source to capture a single whitelight image of the target object (the second type of image). Further,the second light source also gets turned off.

Upon acquiring the plurality of first type of images and a second typeof image, in an embodiment of the present disclosure, at step 504, theone or more hardware processors 118C are configured to perform, one ormore masking operations on the second type of image to obtain a maskedimage. FIG. 8 shows an example of a masked image of the target objectaccording to some embodiments of the present disclosure. In this case,the target object assumed is a seed. In an embodiment, the one or moremasking operations performed on the second type of image to obtain themasked image include background subtraction and thresholding. In otherwords, from the white light image captured by the camera, green backdropis separated out to obtain the masked image of the seed. Further,background subtraction is performed in which portion covered with seedin the white light image is assigned a pixel value 1 and becomes white.Remaining portion is assigned with a value 0 and appears black. Itshould be appreciated that use of any other similar technique is wellwithin the scope of this disclosure. As shown in FIG. 8 , the maskedimage for a seed represents a seed portion mask. Further, in anembodiment of the present disclosure, at step 506, the one or morehardware processors 118C are configured to obtain, by performing anautomated speckle activity map based dynamic speckle analysis techniqueon the plurality of first type of images, a third type of image. Inother words, from the plurality of laser images captured, a singlebinary color image is obtained by applying the automated speckleactivity map based dynamic speckle analysis technique. The single binarycolor image is referred as the third type of image and shows area of oneor more activities occurring within the seed. In the binary color image,areas with activity and no activities could be shown with differentcolors such as light shaded color may indicate presence of activity anddark shaded may indicate no activity. In an embodiment, obtaining thethird type of image by performing the automated speckle activity mapbased dynamic speckle analysis comprises: determining absolutedifference between consecutive images in the plurality of first type ofimages to obtain a plurality of difference images. In an embodiment,laser image frames are assumed of dimension X, Y and N, where X and Yare height and width of the laser image frame respectively and Nrepresent number of temporal frames. Here, the laser image frames arerepresented by I₀, I₁ . . . I_(N-1), I_(N). Then, adjacent frames fromthe laser images frames are subtracted and their absolute values arecalculated as D₀=|I₀−I₁| . . . D_(N-1)=|I_(N-1)−I_(N)|. Here, theplurality of difference images is represented by D₀ . . . . D_(N) andare stored as a D matrix. Further, a value to each pixel of each of theplurality of difference images is assigned based on a comparison with afirst threshold. For example, if value of the first threshold is 20(T₀),then each pixel in D₀ . . . . D_(N) is checked and compared with 20(T₀).If intensity of the pixel is greater than 20(T₀), then the pixel isassigned a value 1 else 0. For example, if D₀ (x, y)>20, then D₀ (x,y)=1 else, D₀ (x, y)=0. The process is repeated for all the pixels in Dmatrix of dimension [X,Y,N−1].

In an embodiment, the first threshold is computed by first determining,in a simultaneous manner, a product of the plurality of first type ofimages with the masked image and an inverted masked image to obtain aplurality of first type of masked images and a plurality of second typeof masked images. In an embodiment, the inverted masked image isobtained by performing foreground detection on the second type of image.FIG. 9 shows an example of an inverted masked image of the target objectaccording to some embodiments of the present disclosure. As shown inFIG. 9 , the inverted masked image for a seed represents a seedlessportion mask. The plurality of first type of masked images are obtainedby multiplying each laser frame from the plurality of laser frames withthe masked image (e.g., seed portion mask) and plurality of second typeof masked images by multiplying each laser frame with the invertedmasked image (e.g., seedless portion mask). Further, absolute differencebetween consecutive images in the plurality of first type of maskedimages and the second type of masked images is determined to obtain aplurality of first type of resultant images and a plurality of secondtype of resultant images. In other words, adjacent laser frames for bothseed and seedless output frames are subtracted and absolute values ofN−1 laser frames for both seed and seedless portion mask are determined.Here, the first type of resultant images and the second type ofresultant images represent output images obtained after determiningabsolute values of N−1 laser frames for both seed and seedless portionmask respectively and are of dimensions [X,Y,N−1]. Furthermore, a firsttype of array and a second type of array is obtained by performing aflattening operation on the plurality of first type of resultant imagesand the plurality of second type of resultant images and a cross overpoint of histogram plots of the first type of array and the second typeof array is determined. Here, the dimensions of the first type of arrayand the second type of array is [X*Y*(N−1), 1] and the cross over pointis computed as the first threshold. FIGS. 10A and 10B illustratehistogram plots of a maize seed and a coffee seed respectively tocompute the first threshold according to some embodiments of the presentdisclosure. As can be seen in FIGS. 10A and 10B, a point where seed andseedless histogram plot for the maize seed and coffee seed intersect isselected as the first threshold T₀. In an embodiment, the computedthreshold value T₀ for maize and coffee seed as shown in FIGS. 10A and10B are 9.5 and 20.0 respectively.

Referring back to the steps executed by the automated speckle activitymap based dynamic speckle analysis technique, the plurality ofdifference images having a value assigned to each pixel are added toobtain a resultant image.

In an embodiment, addition of the plurality of difference images isperformed along third dimension of D matrix and the resultant imageobtained is stored as a S matrix of dimension [X, Y]. The In anembodiment, the resultant image represents output of the automatedspeckle activity map based dynamic speckle analysis technique. FIG. 11shows an example of an image obtained as the output of an automatedspeckle activity map based dynamic speckle analysis technique accordingto some embodiments of the present disclosure. Further, based on acomparison of each pixel of the resultant image with a second threshold,the third type of image is obtained. In an embodiment, the secondthreshold lies in a predefined range of 0.5 to 0.7. In an embodiment, Smatrix is normalized with maximum value in S matrix to provide anormalized matrix represented by S_(norm). Every pixel with valuegreater than 0.7 in the S_(norm) matrix is assigned a value landremaining pixels are assigned a value 0.

In an embodiment of the present disclosure, at step 508, the one or morehardware processors 118C are configured to determine, a target image bymultiplying the third type of image with the masked image. In anembodiment, an area indicative of a level of one or more activitiesoccurring within the target object is detected from the target. In anembodiment, prior to step 508, a thresholding operation is performed onthe third type of image. In an embodiment, the one or more activitiesoccurring within the target object may include but not limited tobiological activities such as brownian movement, water moleculemovement, nutrient movement, and/or the like if the target object is aseed. FIG. 12 shows an example of the target image to detect areaindicative of the level of the one or more activities occurring withinthe target object according to some embodiments of the presentdisclosure. As shown in FIG. 12 , area covered in dark shaded areaindicate low level of activity and area covered in light shaded areaindicate high level of activity within the seed.

Experimental Results:

Table 2 provides a performance comparison of the method of presentdisclosure with conventional approaches for different specimens in termsof timing analysis.

TABLE 2 Method of present Specimen Approach 1 Approach 1 Approach 1disclosure Datasets (Second) (Seconds) (Seconds) (Seconds) Fungus 0.423513.814 0.4601 0.2310 (800 × 800 × 132) Maize Seed 0.1144 22.022 0.05720.0804 (490 × 256 × 100) Coffee Seed 0.232 64.297 0.1183 0.1665 (448 ×448 × 100) Brown Bean 4.707 1665.878 1.784 2.613 (1080 × 1920 × 200)It is observed from the timing analysis provided in Table 2 that themethod of the present disclosure is more efficient than approach 1 andapproach 2 for all specimen datasets. Though, the approach 3 is moreefficient in comparison to the method of the present disclosure, visualinformation provided by the method of present disclosure is more thanapproach 3 for all datasets.

FIGS. 13A through 13D provides a performance comparison of the method ofthe present disclosure with traditional methods for maize seed usingvisual maps according to some embodiments of the present disclosure.FIG. 13D provides a visual map representation for analyzing performanceof the method of the present disclosure for maize seed.

Similarly, FIGS. 14A through 14D provides a performance comparison ofthe method of the present disclosure with traditional methods for coffeeseed using visual maps according to some embodiments of the presentdisclosure. FIG. 14D provides a visual map representation for analyzingperformance of the method of the present disclosure for coffee seed.

It is observed form FIGS. 13A through 14D that better visual maps areprovided by the method of present disclosure for both maize seed andcoffee seed are better in comparison to the traditional methods such asGeneral difference, Lasca and Fuji. Further, it is observed that themethod of the present disclosure is more or less qualitative and helpsin enhancing contrast between different activity regions or visualizefeatures better.

The written description describes the subject matter herein to enableany person skilled in the art to make and use the embodiments. The scopeof the subject matter embodiments is defined by the claims and mayinclude other modifications that occur to those skilled in the art. Suchother modifications are intended to be within the scope of the claims ifthey have similar elements that do not differ from the literal languageof the claims or if they include equivalent elements with insubstantialdifferences from the literal language of the claims.

The embodiments of present disclosure herein address unresolved problemof capturing variations in speckle patterns where noise is completelyremoved and dependency on intensity of variations in speckle patterns iseliminated. The present disclosure is directed to a portable speckleimaging system and method for automated speckle activity map baseddynamic speckle analysis. The system and method of the presentdisclosure performs well in terms of time efficiency and visual cues andrequires minimal human intervention.

It is to be understood that the scope of the protection is extended tosuch a program and in addition to a computer-readable means having amessage therein; such computer-readable storage means containprogram-code means for implementation of one or more steps of themethod, when the program runs on a server or mobile device or anysuitable programmable device. The hardware device can be any kind ofdevice which can be programmed including e.g. any kind of computer likea server or a personal computer, or the like, or any combinationthereof. The device may also include means which could be e.g. hardwaremeans like e.g. an application-specific integrated circuit (ASIC), afield-programmable gate array (FPGA), or a combination of hardware andsoftware means, e.g. an ASIC and an FPGA, or at least one microprocessorand at least one memory with software processing components locatedtherein. Thus, the means can include both hardware means and softwaremeans. The method embodiments described herein could be implemented inhardware and software. The device may also include software means.Alternatively, the embodiments may be implemented on different hardwaredevices, e.g. using a plurality of CPUs.

The embodiments herein can comprise hardware and software elements. Theembodiments that are implemented in software include but are not limitedto, firmware, resident software, microcode, etc. The functions performedby various components described herein may be implemented in othercomponents or combinations of other components. For the purposes of thisdescription, a computer-usable or computer readable medium can be anyapparatus that can comprise, store, communicate, propagate, or transportthe program for use by or in connection with the instruction executionsystem, apparatus, or device.

The illustrated steps are set out to explain the exemplary embodimentsshown, and it should be anticipated that ongoing technologicaldevelopment will change the manner in which particular functions areperformed. These examples are presented herein for purposes ofillustration, and not limitation. Further, the boundaries of thefunctional building blocks have been arbitrarily defined herein for theconvenience of the description. Alternative boundaries can be defined solong as the specified functions and relationships thereof areappropriately performed. Alternatives (including equivalents,extensions, variations, deviations, etc., of those described herein)will be apparent to persons skilled in the relevant art(s) based on theteachings contained herein. Such alternatives fall within the scope ofthe disclosed embodiments. Also, the words “comprising,” “having,”“containing,” and “including,” and other similar forms are intended tobe equivalent in meaning and be open ended in that an item or itemsfollowing any one of these words is not meant to be an exhaustivelisting of such item or items, or meant to be limited to only the listeditem or items. It must also be noted that as used herein and in theappended claims, the singular forms “a,” “an,” and “the” include pluralreferences unless the context clearly dictates otherwise.

Furthermore, one or more computer-readable storage media may be utilizedin implementing embodiments consistent with the present disclosure. Acomputer-readable storage medium refers to any type of physical memoryon which information or data readable by a processor may be stored.Thus, a computer-readable storage medium may store instructions forexecution by one or more processors, including instructions for causingthe processor(s) to perform steps or stages consistent with theembodiments described herein. The term “computer-readable medium” shouldbe understood to include tangible items and exclude carrier waves andtransient signals, i.e., be non-transitory. Examples include randomaccess memory (RAM), read-only memory (ROM), volatile memory,nonvolatile memory, hard drives, CD ROMs, DVDs, flash drives, disks, andany other known physical storage media.

It is intended that the disclosure and examples be considered asexemplary only, with a true scope of disclosed embodiments beingindicated by the following claims.

What is claimed is:
 1. A portable laser speckle imaging system,comprising: a power source; a first light source with a holder supportpositioned to emit a beam towards a target object; an image capturingdevice positioned to receive illumination scattered from the targetobject; a second light source positioned in line with the imagecapturing device to enable white illumination on the target object; anattenuator to control intensity of the beam emitted by the first lightsource; a beam expander positioned between the first light source andthe attenuator to control size of the beam emitted by the first lightsource; a polarizer lens positioned between the attenuator and thetarget object to polarize the beam emitted by the first light source;and a controller unit operably connected to the first light source, theimage capturing device, and the second light source, wherein thecontroller unit comprises: one or more data storage devices configuredto store instructions; one or more communication interfaces; and one ormore hardware processors operatively coupled to the one or more datastorage devices via the one or more communication interfaces, whereinthe one or more hardware processors are configured by the instructionsto: acquire, a plurality of first type of images and a second type ofimage of the target object from the image capturing device, wherein theplurality of first type of images are acquired when the target object isilluminated by the first light source and the second type of image isacquired when the target object is illuminated by the second lightsource; perform, one or more masking operations on the second type ofimage to obtain a masked image; obtain, by performing an automatedspeckle activity map based dynamic speckle analysis technique on theplurality of first type of images, a third type of image, whereinobtaining the third type of image by performing the automated speckleactivity map based dynamic speckle analysis comprises: determiningabsolute difference between consecutive images in the plurality of firsttype of images to obtain a plurality of difference images; assigning, avalue to each pixel of each of the plurality of difference images basedon a comparison with a first threshold; adding the plurality ofdifference images having a value assigned to each pixel to obtain aresultant image; and obtaining, based on a comparison of each pixel ofthe resultant image with a second threshold, the third type of image;and determine a target image by multiplying the third type of image withthe masked image, wherein an area indicative of a level of one or moreactivities occurring within the target object is detected from thetarget image.
 2. The system of claim 1, wherein the first threshold iscomputed by: determining, in a simultaneous manner, a product of theplurality of first type of images with the masked image and an invertedmasked image to obtain a plurality of first type of masked images and aplurality of second type of masked images; determining absolutedifference between consecutive images in the plurality of first type ofmasked images and the second type of masked images to obtain a pluralityof first type of resultant images and a plurality of second type ofresultant images; obtaining a first type of array and a second type ofarray by performing a flattening operation on the plurality of firsttype of resultant images and the plurality of second type of resultantimages; and determining a cross over point of histogram plots of thefirst type of array and the second type of array, wherein the cross overpoint is computed as the first threshold.
 3. The system of claim 1,wherein the first light source is a coherent light source that includesa laser light emitter.
 4. The system of claim 1, wherein the secondlight source includes at least one of (i) a ring light that comprises anarray of light emitting diodes arranged in a circular pattern, and (ii)a white light source with a front trasmittive diffuser.
 5. The system ofclaim 1, wherein each of the plurality of first type of image representsa laser speckle image and the second type of image represents a whitelight image.
 6. The system of claim 1, wherein the one or more maskingoperations performed on the second type of image to obtain the maskedimage include background subtraction and thresholding.
 7. The system ofclaim 1, wherein the inverted masked image is obtained by performing aforeground detection on the second type of image.
 8. A processorimplemented method, comprising: acquiring, a plurality of first type ofimages and a second type of image of the target object from the imagecapturing device, wherein the plurality of first type of images areacquired when the target object is illuminated by the first light sourceand the second type of image is acquired when the target object isilluminated by the second light source; performing, one or more maskingoperations on the second type of image to obtain a masked image;obtaining, by performing an automated speckle activity map based dynamicspeckle analysis technique on the plurality of first type of images, athird type of image, wherein obtaining the third type of image byperforming the automated speckle activity map based dynamic speckleanalysis comprises: determining absolute difference between consecutiveimages in the plurality of first type of images to obtain a plurality ofdifference images; assigning, a value to each pixel of each of theplurality of difference images based on a comparison with a firstthreshold; adding the plurality of difference images having a valueassigned to each pixel to obtain a resultant image; and obtaining, basedon a comparison of each pixel of the resultant image with a secondthreshold, the third type of image; and determining, a target image bymultiplying the third type of image with the masked image, wherein anarea indicative of a level of one or more activities occurring withinthe target object is detected from the target image.
 9. The method ofclaim 8, wherein the first threshold is computed by: determining, in asimultaneous manner, a product of the plurality of first type of imageswith the masked image and an inverted masked image to obtain a pluralityof first type of masked images and a plurality of second type of maskedimages; determining absolute difference between consecutive images inthe plurality of first type of masked images and the second type ofmasked images to obtain a plurality of first type of resultant imagesand a plurality of second type of resultant images; obtaining a firsttype of array and a second type of array by performing a flatteningoperation on the plurality of first type of resultant images and theplurality of second type of resultant images; and determining a crossover point of histogram plots of the first type of array and the secondtype of array, wherein the cross over point is computed as the firstthreshold.
 10. The method of claim 8, wherein the first light source isa coherent light source that includes a laser light emitter.
 11. Themethod of claim 8, wherein the second light source includes at least oneof (i) a ring light that comprises an array of light emitting diodesarranged in a circular pattern and (ii) a white light source with afront trasmittive diffuser.
 12. The method of claim 8, wherein each ofthe plurality of first type of image represents a laser speckle imageand the second type of image represents a white light image.
 13. Themethod of claim 8, wherein the one or more masking operations performedon the second type of image to obtain the masked image includebackground subtraction and thresholding.
 14. The method of claim 8,wherein the inverted masked image is obtained by performing a foregrounddetection on the second type of image.
 15. One or more non-transitorycomputer readable mediums comprising one or more instructions which whenexecuted by one or more hardware processors cause: acquiring, aplurality of first type of images and a second type of image of thetarget object from the image capturing device, wherein the plurality offirst type of images are acquired when the target object is illuminatedby the first light source and the second type of image is acquired whenthe target object is illuminated by the second light source; performing,one or more masking operations on the second type of image to obtain amasked image; obtaining, by performing an automated speckle activity mapbased dynamic speckle analysis technique on the plurality of first typeof images, a third type of image, wherein obtaining the third type ofimage by performing the automated speckle activity map based dynamicspeckle analysis comprises: determining absolute difference betweenconsecutive images in the plurality of first type of images to obtain aplurality of difference images; assigning, a value to each pixel of eachof the plurality of difference images based on a comparison with a firstthreshold; adding the plurality of difference images having a valueassigned to each pixel to obtain a resultant image; and obtaining, basedon a comparison of each pixel of the resultant image with a secondthreshold, the third type of image; and determining, a target image bymultiplying the third type of image with the masked image, wherein anarea indicative of a level of one or more activities occurring withinthe target object is detected from the target image.